Identification and discrimination of olive oil adulteration by oblique-incidence reflectivity difference method

Abstract

Adulteration identification of olive oil is an essential issue in the field of food-related research. In this work, oblique-incidence reflectivity difference (OIRD) method was used to recognize adulteration edible oils in olive oil. In order to reduce the impact of errors, the real and imaginary signals of OIRD were averaged. For the single edible oil adulterated in olive oil, Transformer model, Sparrow Search Algorithm-Hybrid Kernel Extreme Learning Machine (SSA-ELM) model and extreme gradient boosting (XGBoost) model were used to establish analysis and discrimination model. Experimental suggested that all models exhibited the high prediction accuracy with determination coefficients (R²) of 0.99. Moreover, and Random Forest (RF) model can not only identify the type of adulterated olive oil, but also quantitatively analyze adulterated edible oils in olive oil, with a R² of 0.98. OIRD method provides a good strategy for solving practical problems in identifying edible oil adulteration.